Course Information
Course title
Causal Inference and Prediction in Econometrics 
Designated for
Curriculum Number
Curriculum Identity Number
Tuesday 3,4(10:20~12:10) 
Restriction: juniors and beyond OR Restriction: MA students and beyond OR Restriction: Ph. D students
The upper limit of the number of students: 50. 
Ceiba Web Server 
Course introduction video
Table of Core Capabilities and Curriculum Planning
Table of Core Capabilities and Curriculum Planning
Course Syllabus
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Course Description

This course is about some fundamental and important ideas in econometrics.

First, the course starts with a review of the basic ideas and history of econometrics.

Second, we discuss the meanings of identification in econometrics. We start with the classical example of identifying simultaneous equations (demand and supply curves). The various identification meanings are closely related to the estimation strategies. We discuss two important types of estimation methods: moment-based and extremum-based methods.

Third, we discuss the endogeneity problems in econometrics, which are common reasons for the failure of identification.

Fourth, we discuss the ideas and differences in the meanings of causality and prediction.

Finally, we discuss frequentist, Bayesian, and Fisherian inferences. This part emphasizes the connection between econometrics and statistics. 

Course Objective
This course is about advanced undergraduate to introductory postgraduate econometrics. After the training in this course, hard-working students will be well-prepared for master or doctoral programs at top universities in Asian and western countries, and will have the ability to conduct basic research. 
Course Requirement
1. Prerequisites
No econometrics knowledge is assumed. Each topic will be developed at the beginner level so that the course is self-contained. But a certain level of mathematical maturity is expected (see Wikipedia for interesting definitions of mathematical maturity). Precisely, the prerequisites are
(1) introductory microeconomics;
(2) basic calculus, linear algebra, probability, and statistics.

Essentially, students are expected to know what are market (competitive and non-competitive), demand, supply, differentiation, integration, optimization (unconstrained and constrained), Lagrange multiplier, matrix, probability, distribution, density, expectation (conditional and unconditional), mean, variance, and covariance.

This course is suitable for those who are interested in econometrics and statistics for social sciences. Students who have no training in economics but have solid background in mathematics and statistics are welcome.

2. Expectation
Students are expected to review and study the theories developed in classes. The examinations essentially test students' understanding of the theories taught in classes.  
Student Workload (expected study time outside of class per week)
Office Hours
1. Eatwell, J., Milgate, M., Newman, P. (Eds.), 1990. The New Palgrave: Econometrics. The Macmillan Press Limited, London.
2. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Microeconometrics. Palgrave Macmillan, Basingstoke.
3. Durlauf, S.N., Blume, L.E. (Eds.), 2010. Macroeconometrics and time series analysis. Palgrave Macmillan, Basingstoke.
4. Hassani, H., Mills, T.C., Patterson, K. (Eds.), 2006. Palgrave Handbook of Econometrics, Volume 1: Econometric Theory. Palgrave Macmillan, New York.
5. Mills, T.C., Patterson, K. (Eds.), 2009. Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics. Palgrave Macmillan, New York.

1. Efron, B., Hastie, T., 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press, Cambridge.
2. Bickel, P.J., Doksum, K.A., 2015. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 1. CRC Press, Boca Raton.
3. Bickel, P.J., Doksum, K.A., 2016. Mathematical Statistics: Basic Ideas and Selected Topics, Volume 2. CRC Press, Boca Raton.
4. Wasserman, L., 2004. All of Statistics: A Concise Course in Statistical Inference. Springer, New York.
5. Wasserman, L., 2010. All of Nonparametric Statistics. Springer, New York.

Treatment effects
1. Lee, M.J., 2005. Micro-Econometrics for Policy, Program, and Treatment Effects. Oxford University Press, New York.
2. Lee, M.J., 2016. Matching, Regression Discontinuity, Difference in Differences, and Beyond. Oxford University Press, New York.

Model selection and model averaging
1. Claeskens, G., Hjort, N.L., 2008. Model Selection and Model Averaging. Cambridge University Press, Cambridge.
2. Konishi, S., Kitagawa, G., 2008. Information Criteria and Statistical Modeling. Springer, New York. 
Designated reading
1. Hayashi, F. 2000. Econometrics. Princeton University Press, Princeton.
2. Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge.
3. Wooldridge, J.M., 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed. The MIT Press, Cambridge.
4. Lee, M.J., 2010. Micro-econometrics: Methods of Moments and Limited Dependent Variables, 2nd ed. Springer, New York.

1. Konishi, S., 2014. Introduction to Multivariate Analysis: Linear and Nonlinear Modeling. CRC Press, Boca Raton. 
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